Education & Competencies (Technical and Behavioral):
• PhD in statistics, biostatistics, data science, epidemiology, biomedical engineering, computer science, quantitative sciences, or related field with 8+ years of relevant experience; or MS with 12+ years of relevant experience. Equivalent combinations should be reviewed with HR.
• Extensive experience in clinical development within the pharmaceutical, biotechnology, or healthcare research environment, with demonstrated ability to influence cross-functional decisions at study, asset, portfolio, or functional level.
• Demonstrated experience contributing to regulatory submissions, health authority interactions, inspection readiness, and submission-oriented analysis, documentation, traceability, and response activities across multiple regulatory agencies or global health authorities.
• Demonstrated experience as a people manager or formal team leader, including coaching, performance input, talent development, workload prioritization, and support for employee engagement and growth.
• Experience providing technical leadership, matrix leadership, vendor oversight, and mentorship across cross-functional, geographically distributed, or externally supported delivery models.
• Track record of advancing analytical strategy, standards, automation, AI/ML-enabled approaches, or modern data science practices in a regulated clinical development and submission environment.
Highest-priority Technical Skills
• Expert knowledge of clinical trial design, drug development, endpoints, estimands, biomarkers, data interpretation, and the role of analytics in clinical decision making, regulatory strategy, and submission support.
• Strong foundation in statistics and quantitative methods, including longitudinal analysis, survival methods, causal reasoning, simulation, predictive modeling, and uncertainty communication for scientific, governance, and health authority audiences.
• Hands-on fluency in R and/or Python, with working knowledge of SAS and SQL; ability to guide reproducible analyses, code quality, version control, reusable workflows, validated delivery practices, and inspection-ready documentation.
• Strong working knowledge of CDISC standards and submission expectations, including SDTM, ADaM, controlled terminology, Define-XML concepts, reviewer guides, traceability, data lineage, and submission-oriented data package requirements.
• Experience integrating and interpreting diverse data sources, including clinical trial, biomarker, real-world, external, imaging, digital health, or other high-dimensional data as appropriate to the portfolio and regulatory context.
• Practical understanding of AI/ML and advanced analytics in regulated clinical development, including model development, validation, documentation, bias/assumption assessment, governance, explainability, and fit-for-purpose deployment in regulatory-relevant settings.
• Deep knowledge of FDA, EMA, PMDA, NMPA, MHRA, ICH-GCP, GxP, data privacy, inspection readiness, and traceability expectations relevant to clinical data, quantitative deliverables, and global submission packages.
• Ability to establish analytical standards, technical expectations, documentation practices, quality review approaches, and submission-readiness controls that enable scalable and inspection-ready delivery across multiple health authorities.
People Leadership & Behavioral Competencies
• Leads with clarity, accountability, inclusion, and enterprise mindset; creates an environment where team members can deliver, grow, collaborate effectively, and uphold regulatory-quality expectations.
• Coaches and develops direct reports and matrixed contributors, providing actionable feedback, supporting career growth, and building future technical, regulatory, submission, and leadership capability.
• Communicates complex quantitative findings clearly to scientific, operational, technical, executive, senior leadership, and health authority-facing audiences.
• Influences across functions without relying solely on direct authority; builds trusted partnerships with clinical, statistical, programming, data management, regulatory, technology, and vendor stakeholders.
• Balances scientific rigor, speed, quality, resource capacity, regulatory risk, submission timelines, and pragmatic delivery; proactively escalates risks with options and recommendations.
• Demonstrates curiosity, continuous improvement, sound judgment, and commitment to advancing modern clinical data science capabilities, developing others, and maintaining submission-ready standards.